PREDATION'S ROLE IN REPEATED PHENOTYPIC AND GENETIC DIVERGENCE OF ARMOR IN THREESPINE STICKLEBACK
Bibliographic record
Abstract
Predator-driven divergent selection may cause differentiation in defensive armor in threespine stickleback: (1) predatory fish and birds favor robust armor, whereas (2) predaceous aquatic insects favor armor reduction. Although (1) is well established, no direct experimental evidence exists for (2). I examined the phenotypic and genetic consequences of insect predation using F(2) families from crosses between freshwater and marine stickleback populations. I measured selection on body size, and size-adjusted spine (dorsal and pelvic) and pelvic girdle length, by splitting juvenile F(2) families between control and insect predation treatments, set in pond enclosures. I also examined the effect of insect predation on Ectodysplasin (Eda), a gene physically linked to quantitative trait loci for lateral plate number, spine length, and body shape. Insect predation resulted in: (1) significant selection for larger juvenile size, and shorter dorsal spine and pelvic girdle length, (2) higher mortality of individuals missing the pelvic girdle, and (3) selection in favor of the low armor Eda allele. Predatory insects favor less stickleback armor, likely contributing to the widespread reduction of armor in freshwater populations. Because size strongly influences mate choice, predator-driven divergent selection on size may play a substantial role in byproduct reproductive isolation and speciation in threespine stickleback.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".